Restrictions on Pesticides and Deliberate Self-Poisoning in Sri Lanka

Key Points Question What is the association between bans of pesticides in Sri Lanka and deliberate self-poisoning hospitalizations and deaths? Findings In this cross-sectional study of 79 780 patients hospitalized for deliberate self-poisoning, the percentage and case fatality of pesticide poisonings showed significant 18% and 67% declines, respectively, following bans of highly hazardous pesticides. Bans of low-toxicity pesticides were not associated with reductions in self-poisonings but were associated with increased case fatality. Meaning These findings suggest that targeted pesticide bans in resource-poor countries may help to reduce hospitalizations for deliberate self-poisoning.


Interrupted time series analysis
We performed an interrupted time series analysis to quantify changes in monthly poisonings after two pesticide bans in January 2012 and January 2015, respectively.See Supplementary Table 1 for detailed timeline of effective restrictions.
Interrupted time series analysis is one of the strongest observational study designs for evaluating the impact of population-level interventions.
Our data are counts (i.e., number of poisoning admissions), with  denoting the expected counts.Thus, we modelled the intervention using a segmented Poisson or negative binomial regression.
The base segmented regression model can be expressed as: log( ) = 0 + 1 × time + 2 × 1 st intervention + 3 × time since 1 st intervention + 4 × 2 nd intervention + 5 × time since 2 nd intervention, where •   is the expected number of poisoning-related hospital admissions at time t.
• 0 is the intercept, or  at time zero.
• 1 is the slope (monthly trend) pre-interventions or change in the number of poisonings per month.
• time is an integer representing the number of months from the start of the study.
• 2 and 4 represent the level change post 1 st intervention in January 2012 and 2 nd intervention in January 2015, respectively.These coefficients give immediate and sustained change after intervention for the duration of the study period i.e., difference between observed and predicted values based on pre-intervention trend.
•  is a dichotomous variable that takes the value of "0" prior to intervention and "1" otherwise.
• 3 and 5 are the changes in slope, indicating a gradual monthly change in poisonings after 1 st ban and 2 nd ban, respectively. 1,2time s  is an integer taking the value of "0" prior to intervention and increasing by 1 from the date of intervention.
A regression model assumes that the errors are independent i.e., not serially correlated.However, this assumption can potentially be violated in time series data when residual autocorrelation and/or seasonality is present.The interrupted time series models included hospital fixed effects.Inferences were computed with robust standard errors to account for heteroscedasticity and autocorrelation.We used a combination of the Durbin-Watson test, and the autocorrelation function and partial autocorrelation function plots to test for the presence of autocorrelation within hospital sites in our models.If any of these tests indicated that autocorrelation is present, we used Newey-West and Driscoll and Kraay standard errors, using the vcovPL function from the sandwich R-package, to adjust for autocorrelation within clusters (hospital sites). 3tocorrelation was detected in all statistical tests.We investigated for seasonality by (1) including no seasonality, (2) including dummy variables representing the months and (3) including Fourier terms in our models.The dummy variable for each month takes a value of "1" in that month and "0" otherwise.The Fourier terms were included in the form of sin(2⁄12) and cos (2⁄12) pairs to account for monthly data.We used the Akaike Information Criterion (AIC) to choose the most appropriate model for seasonality.The model with the lowest AIC was deemed as the most parsimonious suitable model.Based on AIC, seasonality in the form of Fourier terms was added to the models for all outcomes.
To account for overdispersion, we used combination of checking residual deviance by dividing the residual deviance by the degrees of freedom, and hypothesis testing by using the dispersiontest function from the AER R-package. 4,5If overdispersion was detected in a model based on statistical test and large residual deviance ratio, we applied negative binomial regression.All the analyses were performed in R version 4.0.1.
We further investigated the changes in self-poisoning after pesticide bans on the number of poisonings with pesticide and other substance groups after adjusting for age and sex.Age was defined in 3 categories of 0-24, 25-64 years and ≥ 65 years.We also investigated these changes stratified by sex and age (Supplementary Table 5).

eFigure 1 .eFigure 5 .
Abbreviations: N, Number of patients; S, Number of substances recorded (unknowns were recorded as one substance); DN, Number of deaths; DS, Number of deaths recorded against substances (deaths counted against each substance ingested).

eTable 1 .
Dates of Initiation of Phased-In Partial Restrictions and Subsequent Importation Bans and Legislative Restrictions on Pesticides in Sri Lanka Characteristics of Cohort, Overall and by Time Period Case Fatality (95% CI) of Different Pesticides Over Time Segmented Poisson Regression to Investigate Changes in the Monthly Number of Self-Poisonings After the Implementation of Pesticide Bans, Stratified by Age and Sex Segmented Poisson Mixed-Effects Regression With AR(1) a to Investigate Changes in the Monthly Number of Self-Poisonings After the Implementation of Pesticide Bans Adjusted Segmented Poisson Mixed-Effects Regression With AR(1) a to Investigate Changes in the Monthly Number of Self-Poisonings After the Implementation of Pesticide Bans, Adjusted for Age and Sex Segmented Poisson Mixed-Effects Regression With AR(1) a to Investigate Changes in the Monthly Case Fatality of Pesticide Groups After the Implementation of Bans Large CI due to low number of deaths in this category.Note: Statistically significant RRs are indicated in bold.Segmented Poisson Regression to Investigate Changes in the Monthly Number of Self-Poisonings After the Implementation of Pesticide Bans, Between January 2002 and December 2016 Segmented Poisson Regression to Investigate Changes in the Monthly Case-Fatality of Pesticide Groups After the Implementation of Bans, Between January 2002 and December 2016 8,9egional restriction b In 2018 reregistered with tight restrictions Note: This table is acquired from our cohort description article.7Boldedagentsrestricted to reduce risks from acute poisoning, other restrictions for environmental or chronic toxicity.Internet sources8,9: https://doa.gov.lk/SCPPC/images/ROP/Tabel.pdfhttps://www.parliament.lk/uploads/documents/paperspresented/performance-report-department-of-agriculture-2015.pdfeTable 2. Number of Patients by Study Hospitals Over Time © 2024 Noghrehchi F et al.JMA Network Open.eTable 4. © 2024 Noghrehchi F et al.JAMA Network Open.eTable 5. Abbreviations: RR, rate ratio; CI, confidence interval.aAR(1):Autoregressive of order 1.Note: Statistically significant RRs are indicated in bold.eTable 7. Abbreviations: RR, rate ratio; CI, confidence interval.aAR(1):Autoregressive of order 1. b © 2024 Noghrehchi F et al.JAMA Network Open.eTable 9. Abbreviations: RR, rate ratio; CI, confidence interval.aRRs are adjusted for age and sex. Ne: Statistically significant RRs are indicated in bold.eTable10. Abbeviations: RR, rate ratio; CI, confidence interval.a Large CI due to low number of deaths in this category.Note: Statistically significant RRs are indicated in bold.